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Author: Admin | 2025-04-28
Online marketing users' participation preference feature attribute based on social network text mining is proposed. Firstly, the TF-IDF algorithm is used to calculate the weight value of keywords in the tag, and then the user portrait of the social network platform is constructed after sorting. Then, the collaborative filtering algorithm is used to determine the user's preference characteristics for products containing keywords, and the K-L feature compressor is used to extract the user's participation preference characteristics of online marketing. Finally, the online marketing user participation preference characteristic attributes are classified to realise the analysis of online marketing user participation preference characteristic attributes. The experimental results show that the accuracy of this method is always above 90% and the average time is 3.88s. Keywords: social networks; text mining; online marketing; preferential features; TF-IDF algorithm.DOI: 10.1504/IJBIDM.2025.10067625Special Issue on: Deep Learning Technology and Big Data Method for Business Intelligence and Management Enhancing multiple document summarisation with DNETCNN and BCHOA techniques by Mamatha Mandava, Surendra Reddy Vinta Abstract: Multi-document summarising (MDS) is a helpful method for information aggregation that creates a clear and informative summary from a collection of papers linked to the same subject. Due to the significant number of information available online, it might be challenging to extract the needed information from an internet source these days. To generate the summary, we propose the binary chimp optimisation algorithm (BChOA) in this research. Several preprocessing techniques utilised to remove unwanted terms from the content. Then, for word embedding, FastText is used. The semantic and synthetic features are extracted using the DarkNet-53 and ConvNeXt methods. Using a darknet convolutional neural network (DNetCNN), the features derived from the syntactic and semantic features are concatenated. The Movie review dataset contains 2000 review files, and the BBC news dataset has 50 unique documents. Finally, the outcome demonstrates that our model compares to cutting-edge solutions in terms of semantics and syntactic structure. Keywords: multi-document summarisation; MDS; binary chimp optimisation algorithm; BChOA; ConvNeXt approach; darknet convolutional neural network; DNetCNN.DOI: 10.1504/IJBIDM.2025.10067365 Learning from high-dimensional unlabelled data with outliers: a novel robust approach by Abdul Wahid Abstract: This paper investigates the problem of feature selection and classification under the presence of multivariate outliers in high-dimensional unlabelled data. The research question is how to identify outliers and deal with them in unsupervised learning to improve the clustering accuracy compared with the state-of-the-art non-robust learning techniques. For this purpose, a robust method is proposed by utilise the Mahalanobis distance for outlier identification based on the minimum regularised covariance determinants approach. Furthermore, a new weighting scheme based on Mahalanobis distance is developed for dealing with outlying data points. Finally, it is suggested to combine the proposed weight function and least squared loss function along
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